import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   Optional[int]							, lowercase_				:   Union[str, "sqlalchemy.sql.Selectable"]							, lowercase_				:   Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]							, lowercase_				:   Optional[Features] = None							, lowercase_				:   str = None							, lowercase_				:   bool = False							, **lowercase_				:   List[str]							, )						->	Dict:


   """simple docstring"""







   super().__init__(features=lowercase_							, cache_dir=lowercase_							, keep_in_memory=lowercase_							, **lowercase_)
   _UpperCamelCase           =    Sql(
       cache_dir=lowercase_							, features=lowercase_							, sql=lowercase_							, con=lowercase_							, **lowercase_							, )







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    None
   _UpperCamelCase           =    None
   _UpperCamelCase           =    None
   _UpperCamelCase           =    None

   self.builder.download_and_prepare(
       download_config=lowercase_							, download_mode=lowercase_							, verification_mode=lowercase_							, base_path=lowercase_							, )

   # Build dataset for splits
   _UpperCamelCase           =    self.builder.as_dataset(
       split="train"							, verification_mode=lowercase_							, in_memory=self.keep_in_memory)
   return dataset




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Dataset							, lowercase_				:   str							, lowercase_				:   Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]							, lowercase_				:   Optional[int] = None							, lowercase_				:   Optional[int] = None							, **lowercase_				:   Dict							, )						->	Tuple:


   """simple docstring"""







   if num_proc is not None and num_proc <= 0:
     raise ValueError(f'num_proc {num_proc} must be an integer > 0.')

   _UpperCamelCase           =    dataset
   _UpperCamelCase           =    name
   _UpperCamelCase           =    con
   _UpperCamelCase           =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
   _UpperCamelCase           =    num_proc
   _UpperCamelCase           =    to_sql_kwargs







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    self.to_sql_kwargs.pop("sql"							, lowercase_)
   _UpperCamelCase           =    self.to_sql_kwargs.pop("con"							, lowercase_)
   _UpperCamelCase           =    self.to_sql_kwargs.pop("index"							, lowercase_)

   _UpperCamelCase           =    self._write(index=lowercase_							, **self.to_sql_kwargs)
   return written







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   Tuple)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    args
   _UpperCamelCase           =    {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
   _UpperCamelCase           =    query_table(
       table=self.dataset.data							, key=slice(lowercase_							, offset + self.batch_size)							, indices=self.dataset._indices							, )
   _UpperCamelCase           =    batch.to_pandas()
   _UpperCamelCase           =    df.to_sql(self.name							, self.con							, index=lowercase_							, **lowercase_)
   return num_rows or len(lowercase_)







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   int							, **lowercase_				:   str)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    0

   if self.num_proc is None or self.num_proc == 1:
     for offset in logging.tqdm(
         range(0							, len(self.dataset)							, self.batch_size)							, unit="ba"							, disable=not logging.is_progress_bar_enabled()							, desc="Creating SQL from Arrow format"							, ):
       written += self._batch_sql((offset, index, to_sql_kwargs))
   else:
     _UpperCamelCase      ,						_UpperCamelCase           =    len(self.dataset), self.batch_size
     with multiprocessing.Pool(self.num_proc) as pool:
       for num_rows in logging.tqdm(
           pool.imap(
               self._batch_sql							, [(offset, index, to_sql_kwargs) for offset in range(0							, lowercase_							, lowercase_)]							, )							, total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							, unit="ba"							, disable=not logging.is_progress_bar_enabled()							, desc="Creating SQL from Arrow format"							, ):
         written += num_rows

   return written





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )




